State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China.
Environ Sci Technol. 2013 Apr 16;47(8):3752-60. doi: 10.1021/es304310k. Epub 2013 Apr 1.
As heavy metals occur naturally in soils at measurable concentrations and their natural background contents have significant spatial variations, identification and apportionment of heavy metal pollution sources across large-scale regions is a challenging task. Stochastic models, including the recently developed conditional inference tree (CIT) and the finite mixture distribution model (FMDM), were applied to identify the sources of heavy metals found in the surface soils of the Pearl River Delta, China, and to apportion the contributions from natural background and human activities. Regression trees were successfully developed for the concentrations of Cd, Cu, Zn, Pb, Cr, Ni, As, and Hg in 227 soil samples from a region of over 7.2 × 10(4) km(2) based on seven specific predictors relevant to the source and behavior of heavy metals: land use, soil type, soil organic carbon content, population density, gross domestic product per capita, and the lengths and classes of the roads surrounding the sampling sites. The CIT and FMDM results consistently indicate that Cd, Zn, Cu, Pb, and Cr in the surface soils of the PRD were contributed largely by anthropogenic sources, whereas As, Ni, and Hg in the surface soils mostly originated from the soil parent materials.
由于重金属在土壤中以可测量的浓度自然存在，并且其自然背景含量具有显著的空间变化，因此识别和分配大面积区域的重金属污染源是一项具有挑战性的任务。随机模型，包括最近开发的条件推理树（CIT）和有限混合分布模型（FMDM），被应用于识别中国珠江三角洲表层土壤中重金属的来源，并分配自然背景和人类活动的贡献。基于与重金属的来源和行为相关的七个特定预测因子（土地利用、土壤类型、土壤有机碳含量、人口密度、人均国内生产总值以及采样点周围道路的长度和类别），成功地为来自超过 7.2×10^4 km^2 区域的 227 个土壤样本的 Cd、Cu、Zn、Pb、Cr、Ni、As 和 Hg 浓度开发了回归树。CIT 和 FMDM 的结果一致表明，PRD 表层土壤中的 Cd、Zn、Cu、Pb 和 Cr 主要来自人为源，而表层土壤中的 As、Ni 和 Hg 主要来自土壤母质。
